PMTDS

a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer

Varshini Vasudevaraja, Jamie Renbarger, Ridhhi Girish Shah, Garrett Kinnebrew, Murray Korc, Limei Wang, Yang Huo, Enze Liu, Lang Li, Lijun Cheng

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Background: Precision medicine attempts to tailor the right therapy for the right patient. Recent progress in large-scale collection of patents’ tumor molecular profiles in The Cancer Genome Atlas (TCGA) provides a foundation for systematic discovery of potential drug targets specific to different types of cancer. However, we still lack powerful computational methods to effectively integrate multiple omics data and protein-protein interaction network technology for an optimum target and drug recommendation for an individual patient. Methods: In this study, a computation method, Precision Medicine Target Drug Selection (PMTDS) based on genetic interaction networks is developed to select the optimum targets and associated drugs for precision medicine style treatment of cancer. The PMTDS system includes three parts: a personalized medicine knowledgebase for each cancer type, a genetic interaction network-based algorithm and a single patient molecular profiles. The knowledgebase integrates cancer drugs, drug-target databases and gene biological pathway networks. The molecular profiles of each tumor consists of DNA copy number alteration, gene mutation, and tumor gene expression variation compared to its adjacent normal tissue. Results: The novel integrated PMTDS system is applied to select candidate target-drug pairs for 178 TCGA pancreatic adenocarcinoma (PDAC) tumors. The experiment results show known drug targets (EGFR, IGF1R, ERBB2, NR1I2 and AKR1B1) of PDAC treatment are identified, which provides important evidence of the PMTDS algorithm’s accuracy. Other potential targets PTK6, ATF, SYK are, also, recommended for PDAC. Further validation is provided by comparison of selected targets with, both, cell line molecular profiles from the Cancer Cell Line Encyclopedia (CCLE) and drug response data from the Cancer Therapeutics Response Portal (CTRP). Results from experimental analysis of forty six individual pancreatic cancer samples show that drugs selected by PMTDS have more sample-specific efficacy than the current clinical PDAC therapies. Conclusions: A novelty target and drug priority algorithm PMTDS is developed to identify optimum target-drug pairs by integrating the knowledgebase base with a single patient’s genomics. The PMTDS system provides an accurate and reliable source for target and off-label drug selection for precision cancer medicine.

Original languageEnglish (US)
Pages (from-to)380-394
Number of pages15
JournalQuantitative Biology
Volume5
Issue number4
DOIs
StatePublished - Dec 1 2017

Fingerprint

Precision Medicine
Computational methods
Computational Methods
Medicine
Cancer
Drugs
Target
Interaction
Pharmaceutical Preparations
Neoplasms
Tumors
Genes
Knowledge Bases
Tumor
Knowledge Base
Atlases
Cells
Proteins
Atlas
Bioelectric potentials

Keywords

  • algorithm
  • biological pathway
  • cancer
  • drug target
  • pancreatic adenocarcinoma
  • precision medicine

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computer Science Applications
  • Modeling and Simulation
  • Applied Mathematics

Cite this

PMTDS : a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer. / Vasudevaraja, Varshini; Renbarger, Jamie; Shah, Ridhhi Girish; Kinnebrew, Garrett; Korc, Murray; Wang, Limei; Huo, Yang; Liu, Enze; Li, Lang; Cheng, Lijun.

In: Quantitative Biology, Vol. 5, No. 4, 01.12.2017, p. 380-394.

Research output: Contribution to journalArticle

Vasudevaraja, V, Renbarger, J, Shah, RG, Kinnebrew, G, Korc, M, Wang, L, Huo, Y, Liu, E, Li, L & Cheng, L 2017, 'PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer', Quantitative Biology, vol. 5, no. 4, pp. 380-394. https://doi.org/10.1007/s40484-017-0126-1
Vasudevaraja, Varshini ; Renbarger, Jamie ; Shah, Ridhhi Girish ; Kinnebrew, Garrett ; Korc, Murray ; Wang, Limei ; Huo, Yang ; Liu, Enze ; Li, Lang ; Cheng, Lijun. / PMTDS : a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer. In: Quantitative Biology. 2017 ; Vol. 5, No. 4. pp. 380-394.
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